Global Population Growth and Decline

Author

Red Team
Axel Goh(2203412)
Jeffrey Mak Chee Hong(2201194)
Tristan Raj Shankar(2203491)
Ruchel Lee Wen Xuan(2202719)
Yong Kai Jie(2203201)

Table of Contents

  • Chosen Visualisation Example
  • Critical Assessment of the Original Visualisation
  • Data Used & Data Preparation Process
  • Steps Taken to improve the plot
  • Final Visualisation Demo

Chosen Data Visualisation Example (Part 1)

Figure 1: Source: U.N. World Population Prospects 2022 Note: Only countries with at least one million people are included.

Chosen Data Visualisation Example (Part 2)

Figure 2: Source: U.N. World Population Prospects 2022 Note: Only countries with at least one million people are included.

General

  • Three variables: country population and fertility rate
  • Depiction of Population through bubble size with a relevant Legend

General

  • Colour Coding to distinguish between countries with regards to Fertility Rate
  • Interactive features such as hover text to see Fertility rate

Strengths

  • Colour Contrast
  • Bubble Sizes
  • Clear Y-Axis

Weaknesses

  • Entire graph not visible without scrolling
  • Population size precision through bubbles
  • Difficult to search for specific countries data
  • Clutter of bubbles

Data Used

  • Loaded the U.N. World Population Prospects 2022 dataset.
  • Decided on the U.N. dataset over the World Bank dataset due to fewer missing fertility values.
  • Used R’s built-in “world” dataset for initial country outlines and mapping context.

Data Prepation Process (Fertility and Population Data)

  • Utilized Columns Population, Fertility Rate for year 2022, and Country Name.
  • Removed NA Columns
  • Converted Fertility Rate to Numeric

Cleaning and processing of data, output top 6 rows

  X                          Country Fertility Population Test CountryCode
1 1                            Niger     6.820  24785.587  NER         NER
2 2                          Somalia     6.312  16801.170  SOM         SOM
3 3                             Chad     6.255  16910.218  TCD         TCD
4 4 Democratic Republic of the Congo     6.156  94374.379  COD         COD
5 5         Central African Republic     5.978   5414.014  CAF         CAF
6 6                             Mali     5.956  21561.299  MLI         MLI

Data Preparation Process (Geographic Data)

  • Joined demographic data with R’s world map by country region.
  • Imported and processed GeoJSON files for detailed country and land borders.
  • Simplified geometries for improved performance and validated with spatial data standards.
  • Standardized data to “MULTIPOLYGON” format for mapping compatibility.
  • Prepared base map layer and custom polygon objects for visualization.
  • Combined spatial and demographic data for mapping fertility and population metrics.
  • Curated data columns for final visual display.

Improvements to Original Plot

  • Changed to a Map Visualisation
  • Utilised ColorBrewer’s palette to colour code different Fertility Rate in a range from 1-7
  • Implemented Sequential Binned Colours for accessibility
  • In built ggplotly modebar to Zoom in the map to look for smaller countries
  • Hover-effect over countries to show fertility rate and population data

Initial Plot

Add ColourBrewer’s colour gradient to the map

Plotting on GGPlotly

Additions to the Plot

  • Line Chart to show correlation between Population Size and Fertility Rate
  • Added a data table that allows you to search for a country & each column can filter by min/max
  • Scrollable scale on the LHS to toggle replacement fertility ranges
  • Button-toggle to show countries below replacement fertility

Usage of Shiny

  • Shiny enables the creation of interactive web applications.
  • Reduce the load on the client-side and speed up the application’s performance.
  • Allow interactions and animation such as the slider and button function to display on our world map:
  • Dynamic Filtering:
  • Conditional Display: